from imutils import contours
import numpy as np
import argparse
import imutils
import cv2
import myutils

ap = argparse.ArgumentParser()
ap.add_argument('-i','--image',required=True,help = 'path to input image!')
ap.add_argument('-t','--template',required=True,help="path to template OCR-A image!!")

args = vars(ap.parse_args())

#### 指定信用卡类型
FIRST_NUMBER = {
    "3":"American Express",
    "4":"Visa",
    "5":"Master Card",
    "6":"Discober Card"
}
### 绘图展示
def cv_show(name,img):
    cv2.imshow(name,img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

### 读取一个模板图像
img = cv2.imread(args['template'])
cv_show('origin tamplate',img)

### 灰度值
ref  = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv_show('gray tamplate',ref)

### 二值化图像
ret,ref = cv2.threshold(ref,10,255,cv2.THRESH_BINARY_INV)
cv_show('two_value tamplate',ref)

### 计算轮廓
contours,hierarchy = cv2.findContours(ref.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
draw_img = img.copy()
cv2.drawContours(draw_img,contours,-1,(0,0,255),2)
cv_show('draw_luokuo',draw_img)
print('轮廓个数为：',np.array(contours).shape)

refcnts = myutils.sort_countours(contours,method='left-to-right')[0]
digits = {}

### 遍历每一个轮廓
for (i,c) in enumerate(refcnts):
    ### 计算外接矩形，并且resize 成合适的大小
    (x,y,w,h) = cv2.boundingRect(c)
    roi = ref[y:y +h,x:x+w]
    roi = cv2.resize(roi,(57,88))

    ### 每一个数字对应一个模板
    digits[i] = roi

### 初始化卷积核
rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))
sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))

### 读取输入图形，预处理
image = cv2.imread(args['image'])
cv_show('origin image',image)
image = myutils.resize(image,width=300)
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
cv_show('gray image',gray)

### 礼帽操作，突出明亮区域
tophat = cv2.morphologyEx(gray,cv2.MORPH_TOPHAT,rectKernel)
cv_show('tophat',tophat)

gradx = cv2.Sobel(tophat,cv2.CV_32F,1,0,ksize=-1)
gradX = np.absolute(gradx)
(minVal, maxVal) = (np.min(gradX), np.max(gradX))
gradX = (255 * ((gradX - minVal) / (maxVal - minVal)))
gradX = gradX.astype("uint8")
print (np.array(gradX).shape)
cv_show('gradX',gradX)

#通过闭操作（先膨胀，再腐蚀）将数字连在一起
gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel)
cv_show('gradX',gradX)
#THRESH_OTSU会自动寻找合适的阈值，适合双峰，需把阈值参数设置为0
thresh = cv2.threshold(gradX, 0, 255,
	cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv_show('bi-thresh',thresh)

#再来一个闭操作

thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel) #再来一个闭操作
cv_show('bi-thresh2',thresh)

# 计算轮廓

threshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

cnts = threshCnts
cur_img = image.copy()
cv2.drawContours(cur_img,cnts,-1,(0,0,255),3)
cv_show('img_lunkuo',cur_img)
locs = []

# 遍历轮廓
for (i, c) in enumerate(cnts):
    #### 计算矩形
    (x, y, w, h) = cv2.boundingRect(c)
    ar = w / float(h)

    # 选择合适的区域，根据实际任务来，这里的基本都是四个数字一组
    if ar > 2.5 and ar < 4.0:

        if (w > 40 and w < 55) and (h > 10 and h < 20):
            #符合的留下来
            locs.append((x, y, w, h))

# 将符合的轮廓从左到右排序
locs = sorted(locs, key=lambda x:x[0])
print(locs)
output = []


# 遍历每一个轮廓中的数字
for (i, (gX, gY, gW, gH)) in enumerate(locs):
    # initialize the list of group digits
    groupOutput = []

    # 根据坐标提取每一个组
    group = gray[gY - 5:gY + gH + 5, gX - 5:gX + gW + 5]
    cv_show('group',group)
    # 预处理
    group = cv2.threshold(group, 0, 255,
        cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
    cv_show('group',group)
    # 计算每一组的轮廓
    digitCnts,hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,
        cv2.CHAIN_APPROX_SIMPLE)
    digitCnts = myutils.sort_countours(digitCnts,
        method="left-to-right")[0]

    # 计算每一组中的每一个数值
    for c in digitCnts:
        # 找到当前数值的轮廓，resize成合适的的大小
        (x, y, w, h) = cv2.boundingRect(c)
        roi = group[y:y + h, x:x + w]
        roi = cv2.resize(roi, (57, 88))
        cv_show('roi',roi)

        # 计算匹配得分
        scores = []

        # 在模板中计算每一个得分
        for (digit, digitROI) in digits.items():
            # 模板匹配
            result = cv2.matchTemplate(roi, digitROI,
                cv2.TM_CCOEFF)
            (_, score, _, _) = cv2.minMaxLoc(result)
            scores.append(score)

        # 得到最合适的数字
        groupOutput.append(str(np.argmax(scores)))

    # 画出来
    cv2.rectangle(image, (gX - 5, gY - 5),
        (gX + gW + 5, gY + gH + 5), (0, 0, 255), 1)
    cv2.putText(image, "".join(groupOutput), (gX, gY - 15),
        cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)

    # 得到结果
    output.extend(groupOutput)

# 打印结果
print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]]))
print("Credit Card #: {}".format("".join(output)))
cv2.imshow("Image", image)
cv2.waitKey(0)